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<p>Near-dry electric discharge machining (ND-EDM) has emerged as an advanced and sustainable technique for precision machining of hard and resilient materials. This study investigates the ND-EDM of EN-31 steel with the objective of optimizing key performance characteristics, including material removal rate (MRR), surface roughness (SR), residual stress (RS), and microhardness (MH). The influence of discharge current, pulse on/off time, gap voltage, and mist pressure was systematically examined using an L27 orthogonal array. Experimental results were analyzed through Taguchi-based Grey Relational Analysis (GRA) in combination with Analysis of Variance (ANOVA) to identify significant process factors. Furthermore, an objective weighting methodology incorporating standard deviation and multi-attribute decision-making (MADM) optimization was employed to establish the most favorable parameter combination. The results confirmed that the <b>A1B1C1D1</b> setup yielded the most balanced machining performance, achieving higher productivity with improved surface integrity. The integration of MADM with ND-EDM provides a novel framework for sustainable machining optimization, demonstrating clear industrial advantages over conventional die-sinking EDM.</p>

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A Comparative Study of Die-Sinking and Near-Dry EDM Using MADM-Based Parameter Optimization

  • 期刊论文

  • 发表日期：

    2025年11月3日

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  • Ajit,
  • Deepak Kumar Bhalla,
  • Sanjay Sundriyal

摘要

Near-dry electric discharge machining (ND-EDM) has emerged as an advanced and sustainable technique for precision machining of hard and resilient materials. This study investigates the ND-EDM of EN-31 steel with the objective of optimizing key performance characteristics, including material removal rate (MRR), surface roughness (SR), residual stress (RS), and microhardness (MH). The influence of discharge current, pulse on/off time, gap voltage, and mist pressure was systematically examined using an L27 orthogonal array. Experimental results were analyzed through Taguchi-based Grey Relational Analysis (GRA) in combination with Analysis of Variance (ANOVA) to identify significant process factors. Furthermore, an objective weighting methodology incorporating standard deviation and multi-attribute decision-making (MADM) optimization was employed to establish the most favorable parameter combination. The results confirmed that the A1B1C1D1 setup yielded the most balanced machining performance, achieving higher productivity with improved surface integrity. The integration of MADM with ND-EDM provides a novel framework for sustainable machining optimization, demonstrating clear industrial advantages over conventional die-sinking EDM.